Bridging the data disconnect

Artificial intelligence has made data more useful and accessible to businesses than ever, but if businesses want to get the most from their data they have to get it in a fit state to use.

There’s a significant disconnect between IT specialists, CEOs, and other parts of the business when it comes to understanding what this means and how ready they are, however.

Research from Informatica found 69% of IT teams rate their data quality as excellent or very good, barely half of business leaders say the same at 51%. The same survey found 57% of business professionals and 30% of IT workers don’t know what it means for data to be ‘AI ready’.

In this special edition of the ITPro Podcast, in association with Informatica, we explore what data excellence is, why it’s important to companies, and how they can achieve it.

Highlights

"What many companies are faced with and accept is data that is less than excellent, data that is 'good enough'. But 'good enough' doesn't always play out well, right? It depends on the use case and it depends on how bad that data is. So what does bad data look like? It's siloed, it's duplicated, incomplete, inconsistent, inaccurate and out of date."

"IT teams see infrastructure progress. They've built that pipeline and they've built the storage, and so they feel like data is good. But business teams have a completely different experience and they experience usability challenges, they can't see or access the quality data that IT has built this infrastructure to deliver. So this reflects the difference between having the data and trusting the data for business decisions."

"At the senior manager level, if you separate the senior managers and the individual contributors, only 8% gave their data quality an excellent writing – which is which is not very good. So this indicates that optimism at the executive level that senior managers just don't experience."

"So AI is used as a solution to manage the quality of data, not just consume the data. It allows for AI powered data validation and can be used to catch many issues proactively, and this includes identifying duplicates and inconsistencies, automating data classification and enrichment. AI can play this really, really powerful role in making customer data more accurate, more complete and usable and really mostly transparent. Knowing what data you have, finding the data and, of course, applying those appropriate safeguards, especially when we talk about customer data, which needs to be needs to be safeguarded."

Footnotes

• Why Your AI Data Initiatives for CX Aren't Getting Business Buy-In
• Only a handful of generative AI projects make it into production – here’s why

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